def construct_blaze_kernel(py_func, signature):
"""
Parameters
==========
function: blaze.function.BlazeFunc
overload: blaze.overloading.Overload
"""
nb_argtypes = [to_numba(a.measure) for a in signature.argtypes]
nb_restype = to_numba(signature.restype.measure)
return frompyfunc(py_func, (nb_argtypes, nb_restype), signature.argtypes)
def converter(blaze_dtype, blaze_argtype):
"""
Generate an element-wise conversion function that numba can jit-compile.
"""
dtype = to_numba(blaze_dtype.measure)
argtype = to_numba(blaze_argtype.measure)
@jit(dtype(argtype))
def convert(value):
return dtype(value)
return convert
def construct_blaze_kernel(py_func, signature):
"""
Parameters
==========
function: blaze.function.BlazeFunc
overload: blaze.overloading.Overload
"""
nb_argtypes = [to_numba(a.measure) for a in signature.argtypes]
nb_restype = to_numba(signature.restype.measure)
return frompyfunc(py_func, (nb_argtypes, nb_restype), signature.argtypes)
def converter(blaze_dtype, blaze_argtype):
"""
Generate an element-wise conversion function that numba can jit-compile.
"""
dtype = to_numba(blaze_dtype.measure)
argtype = to_numba(blaze_argtype.measure)
@jit(dtype(argtype))
def convert(value):
return dtype(value)
return convert
def construct_blaze_kernel(function, overload):
"""
Parameters
==========
function: blaze.function.BlazeFunc
overload: blaze.overloading.Overload
"""
monosig = overload.resolved_sig
argtypes = monosig.argtypes
# Try a numba implementation
py_func, signature = find_impl(function, 'numba', argtypes, monosig)
if py_func is not None:
nb_argtypes = [to_numba(a.measure) for a in signature.argtypes]
nb_restype = to_numba(signature.restype.measure)
return frompyfunc(py_func, (nb_argtypes, nb_restype), argtypes)
# Try an LLVM implementation
py_func, signature = find_impl(function, 'numba', argtypes, monosig)
if py_func is not None:
return BlazeElementKernel(py_func, signature.argtypes)
def leaf_arg(type):
kind = llvm_array.SCALAR
rank = 0
llvmtype = to_numba(type.measure).to_llvm()
tree = Argument(type, kind, rank, llvmtype)
return tree
def leaf_arg(type):
kind = llvm_array.SCALAR
rank = 0
llvmtype = to_numba(type.measure).to_llvm()
tree = Argument(type, kind, rank, llvmtype)
return tree
